Paper Citation Record · LEDGER

Robust Convolution Neural ODEs via Contractivity-promoting regularization

As of 19 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2508.11432.

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2508.11432 v1

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measured 40 of 40 reference resolution

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40 of 40 outbound references displayed

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Outbound references

Observation e9b60b6b-9776-41fb-8f75-64de87dd2b56 · outbound

This paper cites Adversarial attacks and defenses in images, graphs and text: A review,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Adversarial attacks and defenses in images, graphs and text: A review,

Reference 1

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This paper cites Intriguing properties of neural networks.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Intriguing properties of neural networks

Reference 2

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This paper cites Adversarial learning target- ing deep neural network classification: A comprehensive review of defenses against attacks,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Adversarial learning target- ing deep neural network classification: A comprehensive review of defenses against attacks,

Reference 3

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Observation d36861e0-b4fa-4d5a-ae20-96c3de2c20aa · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Explaining and Harnessing Adversarial Examples

Reference 4

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This paper cites Feature purification: How adversarial training performs robust deep learning,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Feature purification: How adversarial training performs robust deep learning,

Reference 5

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This paper cites Distillation as a defense to adversarial perturbations against deep neural networks,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Distillation as a defense to adversarial perturbations against deep neural networks,

Reference 6

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Observation d6e4dd49-a41d-4ae9-8cc2-623967f83e30 · outbound

This paper cites Training robust neural networks using Lipschitz bounds,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Training robust neural networks using Lipschitz bounds,

Reference 7

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This paper cites Robust- ness against adversarial attacks in neural networks using incremental dissipativity,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Robust- ness against adversarial attacks in neural networks using incremental dissipativity,

Reference 8

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This paper cites Stable architectures for deep neural networks,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Stable architectures for deep neural networks,

Reference 9

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Observation ac9a2a7b-ff75-4573-86cb-ee09e1d70d2a · outbound

This paper cites Neural ordinary differential equations,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Neural ordinary differential equations,

Reference 10

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This paper cites Latent ordinary differential equations for irregularly-sampled time series,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Latent ordinary differential equations for irregularly-sampled time series,

Reference 11

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This paper cites Hamiltonian neural networks,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Hamiltonian neural networks,

Reference 12

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This paper cites Safety verification and robustness analysis of neural networks via quadratic constraints and semidefinite programming,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Safety verification and robustness analysis of neural networks via quadratic constraints and semidefinite programming,

Reference 13

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This paper cites Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design

Reference 14

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This paper cites On contraction analysis for non- linear systems,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization On contraction analysis for non- linear systems,

Reference 15

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This paper cites Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview,

Reference 16

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Observation 12c892c6-71d1-4724-a23f-2112269aa3be · outbound

This paper cites Towards Robust Neural Networks via Close-loop Control.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Towards Robust Neural Networks via Close-loop Control

Reference 17

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Observation 4744b7e4-1c92-4d9a-9207-21a54bd1104b · outbound

This paper cites On robustness of neural ordinary differential equations,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization On robustness of neural ordinary differential equations,

Reference 18

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This paper cites Stable neural ode with Lyapunov-stable equilibrium points for defending against adversarial attacks,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Stable neural ode with Lyapunov-stable equilibrium points for defending against adversarial attacks,

Reference 19

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This paper cites LyaNet: A Lyapunov framework for training neural ODEs,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization LyaNet: A Lyapunov framework for training neural ODEs,

Reference 20

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Observation 145d8dba-69a0-4f49-8280-e0985f908f76 · outbound

This paper cites Stable Neural Flows.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Stable Neural Flows

Reference 21

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This paper cites Robust implicit networks via non-Euclidean contractions,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Robust implicit networks via non-Euclidean contractions,

Reference 22

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Observation 7bb93bb0-f0d3-4c79-ba27-0f57dcae3c18 · outbound

This paper cites Contracting implicit recurrent neural networks: Stable models with improved trainability,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Contracting implicit recurrent neural networks: Stable models with improved trainability,

Reference 23

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Observation bd75887d-25ab-4720-b2e3-2ed979be6a1f · outbound

This paper cites Robustness certificates for implicit neural networks: A mixed mono- tone contractive approach,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Robustness certificates for implicit neural networks: A mixed mono- tone contractive approach,

Reference 24

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This paper cites Modeling and Contractivity of Neural-Synaptic Networks with Hebbian Learning.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Modeling and Contractivity of Neural-Synaptic Networks with Hebbian Learning

Reference 25

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Observation c192a7c1-7c1d-4491-83e2-fad3e3459723 · outbound

This paper cites Robust classification using contractive Hamiltonian neural ODEs,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Robust classification using contractive Hamiltonian neural ODEs,

Reference 26

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This paper cites Learning stabilizable nonlinear dynamics with contraction-based regularization,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Learning stabilizable nonlinear dynamics with contraction-based regularization,

Reference 27

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This paper cites Recurrent equilibrium networks: Unconstrained learning of stable and robust dynamical models,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Recurrent equilibrium networks: Unconstrained learning of stable and robust dynamical models,

Reference 28

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Observation f36467c0-1df7-4e89-9816-5d3af391afc0 · outbound

This paper cites Neural Exponential Stabilization of Control-affine Nonlinear Systems.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Neural Exponential Stabilization of Control-affine Nonlinear Systems

Reference 29

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This paper cites Automatic differen- tiation in pytorch,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Automatic differen- tiation in pytorch,

Reference 30

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Robust Convolution Neural ODEs via Contractivity-promoting regularization Bullo, Contraction Theory for Dynamical Systems

Reference 31

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This paper cites Convergent systems vs. incremental stability,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Convergent systems vs. incremental stability,

Reference 32

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Robust Convolution Neural ODEs via Contractivity-promoting regularization Ro- bustness may be at odds with accuracy,

Reference 33

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Robust Convolution Neural ODEs via Contractivity-promoting regularization Unresolved cited work

Reference 34

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This paper cites Goodfellow, Y.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Goodfellow, Y

Reference 35

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Robust Convolution Neural ODEs via Contractivity-promoting regularization Nais-net: Stable deep networks from non-autonomous differential equations,

Reference 36

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Observation a743e0f5-97c1-48eb-aad9-d5c1bc14c43a · outbound

This paper cites Towards the first ad- versarially robust neural network model on MNIST,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Towards the first ad- versarially robust neural network model on MNIST,

Reference 37

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Robust Convolution Neural ODEs via Contractivity-promoting regularization Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 38

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This paper cites Adversarial robustness of stabilized neural ode might be from obfuscated gradients,.

Robust Convolution Neural ODEs via Contractivity-promoting regularization Adversarial robustness of stabilized neural ode might be from obfuscated gradients,

Reference 39

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This paper cites The weight γ for the regularization term (14) is set to 1.

Robust Convolution Neural ODEs via Contractivity-promoting regularization The weight γ for the regularization term (14) is set to 1

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T20:02:26.253196Z

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